Abstract

The performance of robotic manipulators is critical to their widespread use in industry. As manipulators become faster, their potential productivity can rise thus improving the return on the investment required to purchase them. Improving accuracy, on the other hand, increases the range of tasks for which the manipulator is suitable.
The speed and accuracy of a manipulator is partly determined by the capability of the algorithm used to control it. Whilst being a highly non-linear multiple input, multiple output device, however, most industrial controllers are derived on the basis that the robot is a series of independent, linear actuator+ link subsystems. The resulting independent joint controller is simple to design and implement but is limited in its performance as link interactions and the non-linear effects of centrifugal and Coriolis forces degrade the accuracy at high manipulator velocities.
Improvements in the control of manipulators may be made by incorporating a mathematical model of the manipulator in the control algorithm. Control schemes such as `computed torque' incorporate an inverse model of the manipulator to calculate the input torques required to force the end-effector to follow a desired trajectory. The equations of motion required to implement these controllers are large and complex even for relatively simple manipulators.
This thesis explores how bond graph representations of robotic manipulators may be used to automate the implementation of model based controllers. To provide a practical basis for this research the bond graph derived controllers are tested on an experimental rigid, planar, direct drive two-link manipulator. It is shown how the bondgraph for this manipulator, including d.c. motor actuators, can be constructed and used to derive the equations of motion of the manipulator automatically. The bond graph model is then validated by comparing simulations obtained using these equations of motion with experimental data.